Initial commit: ACE-Step UI - Open source music generation interface
This commit is contained in:
fspecii
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#!/usr/bin/env python3
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"""Format lyrics and style using the 5Hz LLM.
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This script uses ACE-Step's format_sample to enhance user input with AI-generated
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music metadata (BPM, duration, key, time signature, enhanced description).
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"""
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import argparse
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import json
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import os
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import sys
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import time
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# Get ACE-Step path from environment or use default
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ACESTEP_PATH = os.environ.get('ACESTEP_PATH', '/home/ambsd/Desktop/aceui/ACE-Step-1.5')
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sys.path.insert(0, ACESTEP_PATH)
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from acestep.llm_inference import LLMHandler
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from acestep.inference import format_sample
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# Global handler
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_llm_handler = None
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def get_llm_handler():
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global _llm_handler
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if _llm_handler is None:
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_llm_handler = LLMHandler()
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# Initialize the LLM with the 0.6B model (lighter on VRAM)
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checkpoint_dir = os.path.join(ACESTEP_PATH, "checkpoints")
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lm_model_path = "acestep-5Hz-lm-0.6B" # Use the smaller 0.6B model
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status, success = _llm_handler.initialize(
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checkpoint_dir=checkpoint_dir,
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lm_model_path=lm_model_path,
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backend="pt", # Use PyTorch backend
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device="cuda",
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offload_to_cpu=True,
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)
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if not success:
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raise RuntimeError(f"Failed to initialize LLM: {status}")
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return _llm_handler
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def format_input(
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caption: str,
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lyrics: str = "",
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bpm: int = 0,
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duration: int = 0,
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key_scale: str = "",
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time_signature: str = "",
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temperature: float = 0.85,
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top_k: int = 0,
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top_p: float = 0.9,
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):
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"""Format caption and lyrics using the LLM."""
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handler = get_llm_handler()
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# Build user metadata for constrained decoding
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user_metadata = {}
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if bpm and bpm > 0:
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user_metadata['bpm'] = int(bpm)
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if duration and duration > 0:
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user_metadata['duration'] = int(duration)
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if key_scale and key_scale.strip():
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user_metadata['keyscale'] = key_scale.strip()
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if time_signature and time_signature.strip():
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user_metadata['timesignature'] = time_signature.strip()
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user_metadata_to_pass = user_metadata if user_metadata else None
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top_k_value = None if not top_k or top_k == 0 else int(top_k)
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top_p_value = None if not top_p or top_p >= 1.0 else top_p
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result = format_sample(
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llm_handler=handler,
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caption=caption,
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lyrics=lyrics,
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user_metadata=user_metadata_to_pass,
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temperature=temperature,
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top_k=top_k_value,
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top_p=top_p_value,
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use_constrained_decoding=True,
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)
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return {
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"success": result.success,
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"caption": result.caption,
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"lyrics": result.lyrics,
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"bpm": result.bpm,
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"duration": result.duration,
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"key_scale": result.keyscale,
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"language": result.language,
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"time_signature": result.timesignature,
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"status_message": result.status_message,
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}
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def main():
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parser = argparse.ArgumentParser(description="Format lyrics and style using ACE-Step LLM")
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parser.add_argument("--caption", type=str, required=True, help="Style/caption description")
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parser.add_argument("--lyrics", type=str, default="", help="Lyrics text")
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parser.add_argument("--bpm", type=int, default=0, help="Optional BPM constraint")
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parser.add_argument("--duration", type=int, default=0, help="Optional duration constraint")
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parser.add_argument("--key-scale", type=str, default="", help="Optional key scale constraint")
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parser.add_argument("--time-signature", type=str, default="", help="Optional time signature constraint")
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parser.add_argument("--temperature", type=float, default=0.85, help="LLM temperature")
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parser.add_argument("--top-k", type=int, default=0, help="LLM top-k sampling")
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parser.add_argument("--top-p", type=float, default=0.9, help="LLM top-p sampling")
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parser.add_argument("--json", action="store_true", help="Output as JSON")
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args = parser.parse_args()
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try:
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start_time = time.time()
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result = format_input(
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caption=args.caption,
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lyrics=args.lyrics,
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bpm=args.bpm,
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duration=args.duration,
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key_scale=args.key_scale,
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time_signature=args.time_signature,
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temperature=args.temperature,
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top_k=args.top_k,
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top_p=args.top_p,
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)
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elapsed = time.time() - start_time
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result["elapsed_seconds"] = elapsed
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if args.json:
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print(json.dumps(result))
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else:
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if result["success"]:
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print(f"Caption: {result['caption']}")
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print(f"Lyrics: {result['lyrics'][:100]}...")
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print(f"BPM: {result['bpm']}")
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print(f"Duration: {result['duration']}")
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print(f"Key: {result['key_scale']}")
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print(f"Time Signature: {result['time_signature']}")
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print(f"Language: {result['language']}")
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else:
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print(f"Error: {result['status_message']}")
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except Exception as e:
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if args.json:
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print(json.dumps({"success": False, "error": str(e)}))
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else:
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print(f"Error: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,284 @@
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#!/usr/bin/env python3
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"""Simple music generation script that works like the Gradio interface.
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This is a wrapper script that calls ACE-Step without modifying the original repo.
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Supports all ACE-Step generation parameters.
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"""
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import argparse
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import json
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import os
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import sys
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import time
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# Get ACE-Step path from environment or use default
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ACESTEP_PATH = os.environ.get('ACESTEP_PATH', '/home/ambsd/Desktop/aceui/ACE-Step-1.5')
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# Add ACE-Step to path
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sys.path.insert(0, ACESTEP_PATH)
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from acestep.handler import AceStepHandler
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from acestep.llm_inference import LLMHandler
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from acestep.inference import GenerationParams, GenerationConfig, generate_music
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# Global handlers (initialized once)
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_handler = None
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_llm_handler = None
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def get_handlers():
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global _handler, _llm_handler
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if _handler is None:
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_handler = AceStepHandler()
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_handler.initialize_service(
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project_root=ACESTEP_PATH,
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config_path="acestep-v15-turbo",
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device="cuda",
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offload_to_cpu=True, # For 12GB GPU
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)
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_llm_handler = LLMHandler() # Create but don't initialize (not enough VRAM)
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return _handler, _llm_handler
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def generate(
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# Basic parameters
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prompt: str,
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lyrics: str = "",
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instrumental: bool = False,
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duration: int = 60,
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bpm: int = 0,
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key_scale: str = "",
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time_signature: str = "",
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vocal_language: str = "auto",
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# Generation parameters
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infer_steps: int = 8,
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guidance_scale: float = 10.0,
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batch_size: int = 1,
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seed: int = -1,
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audio_format: str = "mp3",
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shift: float = 3.0,
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# Task type parameters
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task_type: str = "text2music",
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reference_audio: str = None,
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src_audio: str = None,
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audio_codes: str = "",
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repainting_start: float = 0,
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repainting_end: float = -1,
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audio_cover_strength: float = 1.0,
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instruction: str = "",
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# LM/CoT parameters
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thinking: bool = False,
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lm_temperature: float = 0.85,
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lm_cfg_scale: float = 2.0,
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lm_top_k: int = 0,
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lm_top_p: float = 0.9,
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lm_negative_prompt: str = "",
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use_cot_metas: bool = True,
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use_cot_caption: bool = True,
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use_cot_language: bool = True,
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# Advanced parameters
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use_adg: bool = False,
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cfg_interval_start: float = 0.0,
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cfg_interval_end: float = 1.0,
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# Output
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output_dir: str = None,
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):
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"""Generate music and return audio file paths."""
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handler, llm_handler = get_handlers()
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if output_dir is None:
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output_dir = os.path.join(ACESTEP_PATH, "output")
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os.makedirs(output_dir, exist_ok=True)
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# Build generation params
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params = GenerationParams(
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# Basic
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task_type=task_type,
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caption=prompt,
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lyrics=lyrics if lyrics and not instrumental else "",
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instrumental=instrumental,
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duration=float(duration) if duration > 0 else -1.0,
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bpm=bpm if bpm > 0 else None,
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keyscale=key_scale if key_scale else "",
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timesignature=time_signature if time_signature else "",
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vocal_language=vocal_language if vocal_language else "auto",
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# Generation
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inference_steps=infer_steps,
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guidance_scale=guidance_scale,
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seed=seed if seed >= 0 else -1,
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shift=shift,
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# Task-specific
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reference_audio=reference_audio if reference_audio else None,
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src_audio=src_audio if src_audio else None,
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audio_codes=audio_codes if audio_codes else "",
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repainting_start=repainting_start,
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repainting_end=repainting_end,
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audio_cover_strength=audio_cover_strength,
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instruction=instruction if instruction else "Fill the audio semantic mask based on the given conditions:",
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# LM/CoT
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thinking=thinking,
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lm_temperature=lm_temperature,
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lm_cfg_scale=lm_cfg_scale,
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lm_top_k=lm_top_k,
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lm_top_p=lm_top_p,
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lm_negative_prompt=lm_negative_prompt if lm_negative_prompt else "NO USER INPUT",
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use_cot_metas=use_cot_metas,
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use_cot_caption=use_cot_caption,
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use_cot_language=use_cot_language,
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# Advanced
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use_adg=use_adg,
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cfg_interval_start=cfg_interval_start,
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cfg_interval_end=cfg_interval_end,
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)
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# Build generation config
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config = GenerationConfig(
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batch_size=batch_size,
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audio_format=audio_format,
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use_random_seed=(seed < 0),
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)
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start_time = time.time()
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result = generate_music(handler, llm_handler, params, config, save_dir=output_dir)
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elapsed = time.time() - start_time
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# Extract audio paths from result
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audio_paths = []
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if result.audios:
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for audio in result.audios:
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if isinstance(audio, dict) and audio.get("path"):
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audio_paths.append(audio["path"])
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return {
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"success": True,
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"audio_paths": audio_paths,
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"elapsed_seconds": elapsed,
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"output_dir": output_dir,
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}
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def main():
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parser = argparse.ArgumentParser(description="Generate music with ACE-Step")
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# Basic parameters
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parser.add_argument("--prompt", type=str, required=True, help="Music description")
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parser.add_argument("--lyrics", type=str, default="", help="Lyrics (optional)")
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parser.add_argument("--instrumental", action="store_true", help="Generate instrumental music")
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parser.add_argument("--duration", type=int, default=60, help="Duration in seconds (0 for auto)")
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parser.add_argument("--bpm", type=int, default=0, help="BPM (0 for auto)")
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parser.add_argument("--key-scale", type=str, default="", help="Key scale (e.g., 'C Major')")
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parser.add_argument("--time-signature", type=str, default="", help="Time signature (2, 3, 4, or 6)")
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parser.add_argument("--vocal-language", type=str, default="auto", help="Vocal language code")
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# Generation parameters
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parser.add_argument("--infer-steps", type=int, default=8, help="Inference steps")
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parser.add_argument("--guidance-scale", type=float, default=10.0, help="Guidance scale")
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parser.add_argument("--batch-size", type=int, default=1, help="Batch size")
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parser.add_argument("--seed", type=int, default=-1, help="Random seed (-1 for random)")
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parser.add_argument("--audio-format", type=str, default="mp3", choices=["mp3", "flac", "wav"])
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parser.add_argument("--shift", type=float, default=3.0, help="Timestep shift factor")
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# Task type parameters
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parser.add_argument("--task-type", type=str, default="text2music",
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choices=["text2music", "cover", "repaint", "lego", "extract", "complete"],
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help="Generation task type")
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parser.add_argument("--reference-audio", type=str, default=None, help="Reference audio path for style transfer")
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parser.add_argument("--src-audio", type=str, default=None, help="Source audio path for audio-to-audio")
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parser.add_argument("--audio-codes", type=str, default="", help="Audio semantic codes")
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parser.add_argument("--repainting-start", type=float, default=0, help="Repainting start time (seconds)")
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parser.add_argument("--repainting-end", type=float, default=-1, help="Repainting end time (seconds)")
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parser.add_argument("--audio-cover-strength", type=float, default=1.0, help="Reference audio strength (0-1)")
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parser.add_argument("--instruction", type=str, default="", help="Task instruction prompt")
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# LM/CoT parameters
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parser.add_argument("--thinking", action="store_true", help="Enable Chain-of-Thought reasoning")
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parser.add_argument("--lm-temperature", type=float, default=0.85, help="LLM temperature")
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parser.add_argument("--lm-cfg-scale", type=float, default=2.0, help="LLM guidance scale")
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parser.add_argument("--lm-top-k", type=int, default=0, help="LLM top-k sampling")
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parser.add_argument("--lm-top-p", type=float, default=0.9, help="LLM top-p sampling")
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parser.add_argument("--lm-negative-prompt", type=str, default="", help="LLM negative prompt")
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parser.add_argument("--no-cot-metas", action="store_true", help="Disable CoT for metadata")
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parser.add_argument("--no-cot-caption", action="store_true", help="Disable CoT for caption")
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parser.add_argument("--no-cot-language", action="store_true", help="Disable CoT for language")
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# Advanced parameters
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parser.add_argument("--use-adg", action="store_true", help="Use Adaptive Dual Guidance")
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parser.add_argument("--cfg-interval-start", type=float, default=0.0, help="CFG interval start")
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parser.add_argument("--cfg-interval-end", type=float, default=1.0, help="CFG interval end")
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# Output
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parser.add_argument("--output-dir", type=str, default=None, help="Output directory")
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parser.add_argument("--json", action="store_true", help="Output as JSON")
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args = parser.parse_args()
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try:
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result = generate(
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# Basic
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prompt=args.prompt,
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lyrics=args.lyrics,
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instrumental=args.instrumental,
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duration=args.duration,
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bpm=args.bpm,
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key_scale=args.key_scale,
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time_signature=args.time_signature,
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vocal_language=args.vocal_language,
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# Generation
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infer_steps=args.infer_steps,
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guidance_scale=args.guidance_scale,
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batch_size=args.batch_size,
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seed=args.seed,
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audio_format=args.audio_format,
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shift=args.shift,
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# Task type
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task_type=args.task_type,
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reference_audio=args.reference_audio,
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src_audio=args.src_audio,
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audio_codes=args.audio_codes,
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repainting_start=args.repainting_start,
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repainting_end=args.repainting_end,
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audio_cover_strength=args.audio_cover_strength,
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instruction=args.instruction,
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# LM/CoT
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thinking=args.thinking,
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lm_temperature=args.lm_temperature,
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lm_cfg_scale=args.lm_cfg_scale,
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lm_top_k=args.lm_top_k,
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lm_top_p=args.lm_top_p,
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lm_negative_prompt=args.lm_negative_prompt,
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use_cot_metas=not args.no_cot_metas,
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use_cot_caption=not args.no_cot_caption,
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use_cot_language=not args.no_cot_language,
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# Advanced
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use_adg=args.use_adg,
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cfg_interval_start=args.cfg_interval_start,
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cfg_interval_end=args.cfg_interval_end,
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# Output
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output_dir=args.output_dir,
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)
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if args.json:
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print(json.dumps(result))
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else:
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print(f"Generated {len(result['audio_paths'])} audio files in {result['elapsed_seconds']:.1f}s:")
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for path in result['audio_paths']:
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print(f" {path}")
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except Exception as e:
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if args.json:
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print(json.dumps({"success": False, "error": str(e)}))
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else:
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print(f"Error: {e}", file=sys.stderr)
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sys.exit(1)
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user